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NOE ( Network Of Excellence ) Project
Project for Fostering and Promoting
Statistical Thinking

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■ History—

Message from Director-General

Since its inauguration on June 5, 1944, the Institute of Statistical Mathematics (ISM) has been engaged in research on statistical theory and applications to elucidate and design behavior and other phenomena of interest.
This year marks the 77th anniversary of the ISM’s establishment. According
to the Japanese tradition of celebrating milestone ages, reaching the age of
77 represents a special occasion called “Kiju,” or “blessed longevity.” We arevery grateful for your support, to which the ISM owes its longevity. Our faculty, researchers, and staff will maintain their commitment to ensure that
the ISM will continue to address new research challenges as well as professional development in the field of statistics.
In today’s world, data are a rich potential resource, similar in that sense to
crude oil. Around the world, people are seeking the ideal form of a datadriven society. The advent of a new information technology (IT) society is
gradually transforming people’s lives. Statistics and data science help generate new knowledge and value from large bodies of data, expanding theboundaries of academic arenas and socioeconomic activities. These situations in Japan and elsewhere call for large numbers of professional statisticians with excellent
data processing skills.
Past Directors-General of the ISM embraced its mission as an inter-university research institute to create cooperative networks of scientists and academic institutions
that advance data science and its social applications. In addition, the ISM contributes
to academic excellence, helps to realize a data-driven society, and trains the next generation of data scientists.
As the 12th Director-General, I am going to follow in their footsteps and guide the ISM
to deepen the fundamental mathematics of data science, create and activate research
networks, cultivate statisticians, and raise the visibility of their work. The recent
achievements of the ISM include the Network Of Excellence (NOE) Project and a pilot
version (Phase 1) of the Data Science Faculty Development Project at the School of
Statistical Thinking. Building on these successes, the ISM will launch a project aimed attraining statistical experts with advanced knowledge and skills to teach at universities.
The global pandemic of COVID-19 that broke out more than a year ago has not yet
been brought under control, partly due to the emergence of new variants. Our battle
against COVID-19 has been protracted and unpredictable. The ISM is adopting innovative and flexible measures to continue research activities during the current
crisis. It is our sincere hope that the outbreak will subside soon and calm daily life will
be restored.
The ISM has a long history of research on mathematical modeling of infectious disease, and has trained many experts in simulations of epidemics and pandemics. To
meet the challenges of this difficult time, the ISM leveraged its features as a research
institute devoted to statistical and mathematical sciences, and launched a research
project in 2020 aimed at preventing the spread of COVID-19. This project seeks to develop statistical and mathematical tools that will play central roles in preventing outbreaks of COVID-19 and similar infectious diseases in the future. The ISM will continue
its research and education activities with the goal of applying statistics to challenging
social issues, pursuing theoretical research to support their practical applications,and equipping statisticians with the skills to deal with these advanced resources. We
cordially ask for your continued support and cooperation.

Hiroe Tsubaki

Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling
The Department of Statistical Modeling works on structural modeling of physical phenomena related to numerous factors,
and conducts research on model-based statistical inference methodologies. By means of model-based prediction and control,modeling of complex systems, and data assimilation, the department aims to contribute to the development of modeling intel­
ligence in many fields.

■ Prediction and Control Group ————————————————————————————————————————————The Prediction and Control Group works on the development and evaluation of statistical models, which function effectively
in terms of prediction and control of phenomena, decision making, and scientific discoveries. These efforts involve data
analysis and modeling related to phenomena that vary across time and space.

■ Complex System Modeling Group —————————————————————————————————————————The Complex System Modeling Group conducts studies aimed at discovering the structures of complex systems, such as
nonlinear systems and hierarchical networks, through statistical modeling. For these purposes, the group also considers
Monte Carlo simulations, discrete mathematics, and computer science.

■ Data Assimilation Group ———————————————————————————————————————————————The Data Assimilation Group works on the development of data assimilation techniques, which are procedures aimed
at combining information derived from large amounts of observations and a numerical simulation model. By developing
computational algorithms and high-performance parallel computing systems, the group aims to build a next-generation
simulation model that can predict the future in real time.

Department of Statistical Data Science
The Department of Statistical Data Science conducts research on data design methods aimed at managing uncertainty
and incompleteness of information, quantitative methods for evidence-based practice, and related data analysis methods.
Moreover, the department investigates methods for inferring the latent structures in target phenomena from observation data.

■ Survey Science Group ————————————————————————————————————————————————The Survey Science Group promotes research on the design of statistical surveys, development of statistical analysis methods on survey data, privacy protection in official statistics, and their applications. By exploring complex phenomena in various
fields, the group also aims to contribute to practical applications in academia and policy-making through social surveys.

■ Metric Science Group —————————————————————————————————————————————————The Metric Science Group conducts research aimed at identifying and evaluating statistical evidence through quan­tification of phenomena that have not been measured thus far, as well as efficient information extraction from large
databases. The group investigates related methods and develops methods for analyzing the collected data. By working
on applied research in various fields of real science, the group aims to advance practical, applied, statistical mathematicalresearch based on evidence.

■ Structure Exploration Group —————————————————————————————————————————————The Structure Exploration Group conducts research on statistical science aimed at inferring the latent “structure” behindvarious target phenomena in biology, physics, and social science, based on observational data. The group focuseson machine learning, Bayesian reasoning, experimental design methods, and spatial-temporal analysis methods toinvestigate micro/meso/macroscopic and spatial-temporal dynamic structures in target phenomena.

Department of Statistical Inference and Mathematics
The Department of Statistical Inference and Mathematics carries out research into general statistical theory, statisticallearning theory, optimization, and algorithms for statistical inference.

■ Mathematical Statistics Group ———————————————————————————————————————————The Mathematical Statistics Group is concerned with aspects of statistical inference theory, modeling of uncertain
phenomena, stochastic processes and their application to inference, probability and distribution theory, and the related
mathematics.

■ Learning and Inference Group ————————————————————————————————————————————The Learning and Inference Group develops statistical methodologies to describe the stochastic structure of data math­ematically and clarify the potential and the limitations of the data theoretically.

■ Mathematical Optimization Group —————————————————————————————————————————The Mathematical Optimization Group focuses on mathematical theory and practical applications of optimization andcomputational algorithms together with underlying numerical or functional analysis and discrete mathematics.

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NOE-type Research
Risk Analysis Research Center
Risk Analysis Research Center is pursuing a scientific approach to managing uncertainties and risks in society, which haveincreased with the growing globalization of society and economy. Our research projects are mainly seismology, finance,
resources, environmentology, database development and risk mathematics. The Center also manages a network organization
for risk analysis to facilitate research collaboration across different organizations with the common goal of creating a safe andresilient society.

Research Center for Statistical Machine Learning
Machine learning is a research field associated with autonomous systems that can learn their behavior from data. This field
is based on both the statistical science concerning inference from data and computer science concerning efficient algorithms.
It can be applied to broad disciplines ranging engineering and information science to natural science, such as robotics andbrain sciences. Our research center aims at supporting the academic community of this field, as well as producing influential
research through various joint projects.

Data Science Center for Creative Design and Manufacturing
We aim to foster new scientific methods for innovative design and manufacturing. Various fields in manufacturing are now
facing a revolutionary period. Population reduction and globalization are bringing dramatic changes in the industrial structure
in Japan. Countries around the world has actively developed their growth strategies utilizing data science as a driving force.
We have accumulated state-of-the-art technologies in data science here. We are devoted to foster and practice advanced
methods in data science for design and manufacturing.

Research Center for Medical and Health Data Science
Research Center for Medical and Health Data Science aims to facilitate statistical data science research that covers
medical studies, drug developments, health care, and public health. Our research projects involve fundamental mathematics
and compu­tational science for medical applications, applied methodology for basic, clinical and social medicine, and modern
technology such as artificial intelligence, machine learning, and big data analyses. Furthermore, our research center aims at
constructing a research network of the academic community of this field, as well as offering advanced statistical education
programs.

Professional Development
School of Statistical Thinking
The mission of the School of Statistical Thinking is to plan and implement various programs for statistical thinking, fromextension courses to a professional development program. The researchers affiliated with the school are often involved withspecific data analysis projects, which help them to gain hands-on knowledge of data science. We expect such an experienced
researcher will play an active role as a number-cruncher, as a modeler, or as a project coordinator.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists academics and their collaborators in many ways: managing
computer systems and networks, editing and publishing journals, maintaining the library, and managing tutorial programs.

■ Computing Facilities Unit The Computing Facilities Unit is in charge of managing computer facilities and scientificsoftware.

■ Computer Networking Unit The Computer Networking Unit is responsible for computer networking and its infra­
structure, and network security.

■ Information Resources Unit The Information Resources Unit is responsible for maintaining a library and an electronic
repository, and is in charge of planning statistical tutorial programs open to the public.

■ Media Development Unit The Media Development Unit is in charge of publishing and editing of research results
and PR brochures.

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Research Topics
Risk Analysis Research Center

Mathematical Analysis for Random Phenomena■ The stochastic process as a mathematicalexpression of a real phenomenon
A real random phenomenon is expressed as a stochastic process mathematically. The Brownian motion is well-known as a movement of pollen floatingin the water. Let B(t) (t≧0) be the Brownian motion
as a stochastic process. In this case, a random variable B(t) is normally distributed for each t>0 and its
path is a continuous function of t . But, risk analysis
deals with more complicated phenomena.
For example, in a financial crisis or a disastrous
earthquake, huge change occurred suddenly and thisis a distinctive feature of phenomena involving risk.
In such cases, continuous processes are insufficientand stochastic processes with jumps are demanded.
The risk analysis research center cosponsors two
symposiums. Both symposiums aim to be a fusion ofmathematical theory and statistical application as
follows.
■ Infinitely divisible processes and related topics
The symposium started as a workshop of probability theory, but it has developed into a symposiumincluding statistical application. “Infinite divisibility”
is the property of a distribution that could be decomposed into identically distribution for an arbitrary
number. For example, the normal distribution is expressed as the convolution of same n distributions
for arbitrary natural number n.

The author’s webpage announces the information of the cooperative
symposiums.

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Various distributions including the Poisson distribution and the stable distribution have this property.
The stochastic process version is infinitely divisible
process. This research field has been developing asa traditional probability theory. Nowadays, it also becomes an essential tool to construct statistical model
for mathematical finance.

■ Extreme value theory and applications
Extreme value theory deals with extreme events,
like biggest one among various data. A typical application of the extreme value theory is disaster prevention because a disaster is caused by extreme event,not usual one.
For example, a flood is caused by heavy rain in ashort period, not by total precipitation in the longterm. Hence the prediction for heavy rain is essential to defend serious damage and used for design ofdikes or evacuation information etc. Not only in aflood but also in various disasters, a few events dominate most of the total damage. Therefore, it is not
exaggeration, though extreme value theory is thetheory for risk management.
The information on these symposiums is announced on the ISM and my Web site.
Takaaki Shimura

The cooperative research reports and related books.

A New Adjustment Method Using Spurious Correlations for Multiple Testing■ Multiple testing and its adjustment method
To understand risk factors for a certain disease, wewill consider statistically comparing the features in
the group with the disease and the group without thedisease. For simplicity, we assume that there are only
two types of features for a while. Usually, a statistical
test is used for such a comparison, and if it is rejected,
the two groups are considered to be different. Since
there are two types of features, there are also two
tests, which are called multiple testing. In most tests,
a significance level of 5% is used, but if we set the significance level of both tests to 5%, the probability of
mistakenly rejecting the results may become 10% eventhough there is no difference between the two groups.
Therefore, the significance level of each test is reduced

so that the probability of rejection is kept at 5%. Thisis called adjustment for multiple testing. The simplest
method is the Bonferroni method, where the significance level of each test is set to 2.5%. However, thismethod has the problem that the significance level ofeach test is so small that even if there is a difference
between the two groups, it is not easily rejected. If
there is a positive correlation between the features,that is, if there is a positive correlation between the
two tests, we can use this information to give an adjustment method that is easier to reject than the Bonferroni method. This is called the MaxT method, and it
becomes more effective as the value of the positive
correlation increases. Since the value of this correlation is usually not known, it is given by estimation.

■ Proposal of a new adjustment method
When estimating the correlation, mixing two groups
together leads to an unreasonable value. Figure 1
shows the scatter plots for the two groups, red andblue, with the two types of features represented on thevertical and horizontal axes. The true correlation between the features is 0.15, but both features have alarger expectation for the blue group than for the redgroup, and so when mixed together, the estimated correlation becomes 0.30. This is called spurious correlation. This is an unjustified value, but it can be used toadjust for the multiple testing, which is the strange
finding of this study. When both features influence thedisease, the spurious correlation tends to be larger,
making it more effective than the MaxT method. In
fact, for the data in Figure 1, the MaxT method does not
reject both features, while the proposed method using
Figure 1: Synthetic data with 2 groups, 2 features and 10 samples.
the spurious correlation rejects bothfeatures. According to numerical experiments, the effectiveness of the
proposed method tends to become
more pronounced as the number offeatures increases. Figure 2 comparesthe features of purebred mice and its
consomic mice, where each cell corresponds to each test. Dark blue, blue,
light red, and red indicate the tests
rejected by the Bonferroni, MaxT, an
improved MaxT, and the proposed
methods. Although this data was notcompatible with the proposed method, it still rejected 9 new tests.
Figure 2: Results of multiple testing for mice data ( Ninomiya et al. 2021 JSPI 128-138
Yoshiyuki Ninomiya
Fig. 2 ).
5Research Center for Statistical Machine Learning

Geometry of Random Fields —Integral Geometry for Statistical Findings■ Geometry of random fields
Figure 1 (left) depicts an example of image data
(random field). Integral geometry provides a tool tosummarize such data. By choosing a threshold, weconvert the image into binary data (Figure 2 right).
In the binary image data, there are 5 connectedblack areas (including two tiny ones) and two holes
surrounded by black region. The difference χ= 5 − 3 = 2
is called the Euler number. The triplet, the Euler number as well as its area and boundary length, is called
the Minkowski functional and is used for data analysis.

tive universe, and the methods of the Euler number
and the Minkowski functionls are one of the standard
tools. As joint work with researchers in KEK and Kavli
IPMU, we are examining the relationship with the random field structure and the Euler number and the
Minkowski functionls.

Figure 3: Cosmic Microwave Background (CMB)( Copyright PLANCK ).

■ Change point problems
Figure 1: Random field ( left ) and its excursion set ( right ).

■ Analysis of CMB data
In Figure 2, for two random fields, the Euler numbers are drawn in blue by changing the threshold v.
The orange curves are expected Euler number calculated theoretically by assuming the “Gaussian assumption”. One may find that in the left panel two curves
are close to each other, but are not in the right panel.
Actually, in the left panel the random field was generated by Gaussian random variables, and was not in theright panel.
In this way, the structure of the random field can beinvestigated by the Euler number. The cosmic microwave background in Figure 3 is microwave observed inall directions of the sky, representing the temperaturefluctuation of the universe. The study of this random
field is expected to reveal the mechanism of the primi-

The finding of the Higgs boson, the last elemental
particle, was announced on 14th March 2013. Figure 4
is the experimental data published at CERN homepage.
Two curves in red in the figure have small peaks. These
are indeed evidences of the finding, whereas the rational judgment criterion is needed to distinguish true
peaks and stochastic fluctuations (i.e., false positive).
This problem is called change point problem or multiple comparisons in statistics, or LEE (look-elsewhere
effect) in physics. The theory of geometry of random
fields can be utilized in this problem. Genome scan andpeak detection in brain image data are similar problems. We are developing the methodology of general
purpose as well as related mathematics.

Figure 4: Finding of Higgs boson (Copyright CERN ).
Figure 2: Gaussian random field ( left ) and non-Gaussian
random field ( right ).

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Satoshi Kuriki

Research Topics

Interpretation of Trained Deep Neural Networks to Collaborate with Scientists■ The importance of interpretability in scientific
data analysis
In thermodynamics, Gibbs extended the theory ofheat engines to chemical reaction theory. It brought
great development to science. In this way, human scientific insight obtains general principles beyond mere interpolation. However, it is sometimes difficult to apply
this insight to complex systems, such as nonlinear or
nonequilibrium phenomena. Recently, there has been
a lot of research on analyzing and modeling complex
scientific data using machine learning models withhigh-expression ability, such as deep neural networks(DNNs). However, most of them are interpolative models and consist of nonlinear functions with a large number of parameters, making their interpretation extremely difficult. To overcome this deadlock, we are developing
methods to bridge machine learning and human intelligence (Fig. 1). In this paper, we introduce a method forextracting interpretable physical information from a
DNN trained with physics data [Y. Mototake, Phys. Rev.
E, 2021].

■ Symmetry of data manifolds and conservation laws
It has been reported that DNNs have the ability to
extract data distributions as a subspace, like a low-dimensional manifold, to obtain useful information. In
physical systems, the low-dimensional data structureis also related to physical constraints, such as conservation laws or order parameters. In other words, it isexpected that we can extract interpretable physics information, such as conservation laws or order parameters, from the data manifold modeled by the DNNs. In
Hamiltonian systems, Noether’s theorem links the invariance of the system and conservation laws. We theoretically confirmed that the symmetry of a dynamicalsystem can be inferred from the data manifold of physics time-series data and developed a method to extractconservation laws from a trained DNN (Fig. 2).

■ Extraction of conservation laws from trained DNNs
A trained DNN modeling a data manifold could map
the input space into a lower-dimensional manifold (Fig.
3). Therefore, in the middle layer of the DNN, the input
space of the outer region of the manifold disappears.
Based on this picture, we can identify whether a region
in the input space is on or off the manifold. Using thisidentification method, we proposed a method to extract symmetric coordinate transformations of the data
manifold. It was confirmed that the proposed method
could infer a conservation law for circular motion or
the collective behavior of living things. In the future, weplan to advance the method to realize new physicaldiscoveries or efficient learning methods.
Yoh-ichi Mototake

Figure 1: Collaboration between machine learning and humans.

Figure 2: Extraction of interpretable and usable information fromtrained DNNs.

Figure 3: Symmetry extraction and conservation estimation fromtrained DNNs using the proposed method.

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Data Science Center for Creative Design and Manufacturing

Search for New Quasicrystals Using Machine Learning■ Accelerating the discovery of new quasicrystals
based on data science
Quasicrystals form a class of materials that do nothave the translational symmetry of ordinary periodic
crystals, but have a high degree of order in their atomic arrangement. The first quasicrystal was discoveredby Schechtman in 1984. Over the next 35 years, morethan 100 types of quasicrystals were found, and quasicrystals were placed as the third class of solids alongwith ordinary crystals and amorphous. However, thepace of the discovery has slowed significantly in recent years. We are participating in the Grant-in-Aid for
Scientific Research on Innovative Areas, "Hypermaterials: Innovation of Materials Science in Hyperspace"),which started in 2019 (Prof. Ryuji Tamura, Tokyo University of Science). We will accelerate the discovery of
new quasicrystals by introducing machine learningtechniques to the field.

■ Prediction of chemical compositions to form
quasicrystals
We tackled the problem of predicting quasicrystals
using a simple workflow of supervised learning. The
input variable of the model is the chemical composition. The output variables represent class labels indicating "quasicrystals", "approximant crystals", and"others" including ordinary periodic crystals. As training data, we used the chemical compositions of quasic-rystals, approximants, and ordinary crystals that havebeen discovered so far. We evaluated the predictabilityof machine learning models for the three-class classification task. The predicted quasicrystalline phases
were compared with known experimental phase diagrams, and the precision and recall of the phase prediction reached 0.793 and 0.714, respectively. We aim
to use this model to narrow down the candidate compositions that form quasicrystals and approximants.

■ Rules for quasicrystal formation discovered bymachine learning
Furthermore, by extracting the input-output rules
inherent in the black-box model of machine learning,
we have uncovered hidden laws on the formation ofquasicrystalline phases. These rules are expressed infive equations for the van der Waals radius of consistent elements and the itinerant valence electron concentration. These conditions provide design guidelines for the search for new quasicrystals. This may
open the door to a better understanding of the stabilityof quasicrystals, a central issue in condensed matterphysics.

■ Towards discovery of innovative quasicrystals
With this work, we have taken the first step towardrealizing quasicrystal discovery through data science.
We are now using our model in the synthetization of
new quasicrystals. During nearly 35 years after the discovery of thefirst quasicrystal, little
is known about the formation mech­anism ofquasicrystals. Data science may make a significant contribution tosolving this unsolved
issue in quasicrystal research.
Ryo Yoshida

Figure:Formation rules on quasicrystalline phases thatwere discovered by machine learning.

8Research Topics

Creation of Polymer Properties Database Using Automated Molecular
Dynamics Simulation■ Understanding and discovery of thermallyconductive polymers
Organic polymers are materials with huge molecular weight, and have unique characteristics of lightness, ease of processing, and flexibility. The thermal
conductivity of polymers is much lower than that ofmetals and ceramics. In recent years, the applicationof polymers to electronic devices, such as 5G wireless device, has required to improve heat dissipation. Thus, much attention has been paid to the studyof high thermal conductive polymers. Our group isparticipating in JST-CREST Nano-enabled ThermalManagement.
■ Machine learning technologies for automatedmolecular design
We applied a machine learning to the polymer database PoLyInfo to design high thermal conductive
polymers. For thermal conductivity of amorphous
polymers, only 28 samples were recorded at the database. To overcome the difficulty of the limited data,
we applied a transfer learning to obtain a predictivemodel of thermal conductivity. By solving its inverse
problem, we created a virtual library of 1,000 candidate polymers with high thermal conductivity. From
this library, we synthesized three new polymers witha thermal conductivity of 0.41 W/mK. This corresponds to an improvement of 80% compared to commonly used amorphous polymers.

■ Lack of open data for data-driven research
The amount of data in materials research is muchless than in other applied fields of data science.
There are three main reasons for this: (1) the highcost of data production; (2) the difficulty of creating
common data due to the diversity of researchers’
needs; and (3) the prevention of information leakage
to competitors. In particular, PoLyInfo is the only polymer database that can be used in data-driven research.
■ Creation of polymer property database with
automated molecular dynamics simulation
We developed a high-throughput calculation system for automated molecular dynamics simulation ofpolymer properties. We are creating a polymer properties database using this automatic system. Our ultimate goal is to build a database that covers various
properties for more than 100,000 polymers. We willobserve the joint distribution of multiple properties
in the vast material space. We aim to comprehensively identify polymer structures around the Pareto
boundaries between multiple properties. Furthermore, novel polymers that can overcome the Pareto
boundary may be discovered. We will accomplish thismission as a joint research project under industryacademia partnerships.
Yoshihiro Hayashi

Figure: Automated calculation of polymer properties using molecular dynamics simulation.

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Research Center for Medical and Health Data Science

Pursuing the Advancement of Medical and Health Data Science■ Medical and health data science and thecenter’s mission
In recent years, expectations regarding data science
have been growing in various fields, including medical
and health sciences. These days, the application of deeplearning and other cutting-edge AI technologies to theanalysis of molecular/medical big data is widely expected to sharply accelerate the elucidation of biological anddisease mechanisms, the development of medical technologies, including drug discovery, and the realization of
precision medicine. In the meantime, we should not forget the critical role of obtaining solid evidence on medical technology from high-quality small data derived
through careful study design and statistical inference.
While it is considered that expectations regarding medical and health data science will continue to grow, theframework of data science has yet to be established to
fully meet such expectations; hence there is a big gap tobe filled. That is to say, the enhancement of education
and research in medical and health data science is oure-learning website

Symposium

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great challenge for the future.
The Research Center for Medical and Health Data Science was established in April 2018 based on researchand researcher education in data science and on the network of Japanese and foreign researchers that the Institute of Statistical Mathematics has established over many
years. The Center’s mission is to promote projects thatenhance education and research in medical and healthdata science in Japan. Although the Center has been in
existence for only two years, it has been involved in avariety of educational and research activities thus far.

■ Educational and research activities
In the area of education, the Center has been promoting the development of various educational programs,
such as systematic education courses on theories andmethods of statistical mathematics, biostatistics, theoretical epidemiology, and machine learning, topics thatform the basis of medical and health data science. In addition, the Center has focused on open lectures and onthe-job-training, as well as e-learning materials
to be shared with the research community.
In the research sector, the Center has been actively working on a variety of projects, such as
those involving statistical methodologies pertaining to advances in medical technology andhealthcare, as well as research in public healthand social medicine. Additionally, the Center isinvolved in big data analysis using cutting-edgemachine learning and AI algorithms, and is performing studies on foundational mathematics
and computer technologies.

■ Medical and Health Data Science
Research Network
All of the above projects are linked with theactivities of the newly launched “Medical and
Health Data Science Research Network.” This
unique network comprises 94 organizations (asof February 2021), including related academic societies, universities, and research institutions inall parts of Japan, as well as hospitals and companies. The substantial advancement of medical
and health data science depends on the organic
collaboration of researchers in the fields of statistical mathematics and information science andthose in the fields of medical and health science.
The Center intends to make every possible effort
to play an important part in bridging these fields.

Shigeyuki Matsui

Research Topics

Data Science Technology for Advanced Medical Researches■ Data science technology required for medical
science and healthcare practices
In modern society, there have been serious problems
concerning human health and medicine, e.g., global pandemic of emerging infectious diseases such as COVID-19
and arrivals of aging societies throughout the world. To
develop effective treatments and diagnostic methods,
and to conduct policy making based on scientific evi-

dence, adequate data analysis methods are required.
Thus, advanced technologies of data science are essential and strongly needed for sustainable medical andhealthcare services. We have an international collaborative network with advanced research universities andinstitutes, and we are proceeding a lot of advanced researches to create scientific evidence that contributes tothe real world medical and society problems.

■ Advanced research synthesis methods
for large scale clinical trials databases

Figure 1: Network meta-analysis for delivery methods of cognitive behavior treatments for major depression. Estimates and 95%
confidence intervals for standardized mean differences arepresented; the reference treatment is care-as-usual (Cuijpers,
Noma et al., JAMA Psychiatry 2019, 76: 700–707).

Figure 2: Survival curves for subgroups expected the tamoxifen treatment is effective that were estimated by new machine learning
methods using gradient boosting tree (Sugasawa and Noma,
Stat Med 2020, 38: 5146–5159).

Network meta-analysis is a novel data science
method for synthesizing past available evidenceof published clinical trials and evaluating thecomparative effectiveness of available treatments. In Figure 1, we present results of a network
meta-analysis that evaluated delivery methodsof cognitive behavior treatment (CBT) for major
depression (individual/group counselling, internet CBT program, etc). This study synthesized alarge clinical trial database based on more than
150 trials using advanced data analysis technology. This study was conducted as an international collaborative work with Vrije Universiteit Amsterdam, Oxford University and Kyoto University,and was graded as ISI Top Paper and Highly Cited
Paper. We have also conducted a lot of collaborative works using these methods based on the international collaborative network.

■ Developments of AI and machine learning
methods for medical technologies
Recently, AI and machine learning methods
have been well studied for developing advanced
medical technologies. In Figure 2, we present results of data analyses for personalized breast
cancer treatments using tamoxifen by newly developed statistical methods using the gradientboosting tree. Using the new methods, we could
identify subgroups that were expected that tamoxifen is more effective than the others. We
also conduct clinical epidemiology studies using
recent AI technologies such as deep learning actively, e.g., we conducted a health technology assessment study for the effectiveness of diabetes
medications using deep neural network as a collaborative study with the University of Tokyo.

Hisashi Noma

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Research Topics
Next-Generation Simulation NOE

Statistical Analysis of Ensemble Data
■ Ensemble data as output of particle filtering
The particle filter (PF) is one of the data assimilation
methods. An advantage of PF is that we can deal withnonlinear models and non-Gaussian noise. With PF, weshould be able to accurately represent probability distribution of state variables. Now, a question we have is how
to handle ensemble data, which is the output of PF. If theensemble represents a complex distribution accurately,
we should extract more information from the ensemble,
rather than conventional sample statistics, such as theensemble mean and the ensemble variance (ensemble

spread). Let us consider how to analyze the ensemble if theensemble may represent a “non-Gaussian” distribution.

■ Higher-order moments
To insist that the ensemble represents non-Gaussian
distribution, let us consider using sample moments ofthe ensemble. If the ensemble represents a Gaussian distribution, the third-order sample moment should be zero.
Then, if the third-order moment is not zero, it means thatthe ensemble does not represent Gaussian. However,there are concerns as follows. Since we compute moments of a finite number of samples, even if the ensemble
is a set of samples from a Gaussian distribution, it is unlikely that the third-order moment will be exactly zero.
Conversely, and miraculously, even if the third-ordermoment becomes zero, can we really conclude that it iszero? If you have another sample, the third-order moment should be non-zero. Then, let us relax the condition
that it becomes exactly zero. Rather, let us consider saying that the ensemble represents a Gaussian distribution
if the third-order moment is small, while that the ensemble represents non-Gaussian if the third-order moment islarge. However, even if we adopt such a condition, howshould we distinguish between large and small? Also, let
us suppose the ensemble represents a bimodal distribution. The third-order moment will be zero, but we do not
like to consider the ensemble to be equivalent to a Gaussian distribution. Apparently, it is not a good idea to analyze ensemble data based on the third-order moment.

■ Claim of non-Gaussian distribution
In the first place, I think it is worthless to claim that“the ensemble represents a non-Gaussian distribution.”
This is because there are many non-Gaussian distributions. We cannot move forward unless we specify an alternative distribution that is non-Gaussian.

■ Model selection of ensemble data

Figure: Temporal variations of probability distribution of various
physical quantities observed during development of cumulonimbus: temperature (PT ), water vapor (QV ), humidity(RH), and vertical wind ( W ) estimated by a numerical weather prediction model by JMA and the particle filter (PF).

12Here, we consider a Gaussian distribution, a Gaussian
mixture distribution, and a histogram; the latter two distributions are alternative distribution models. We selectmost preferable distribution using information criteria.
The figure on the left shows an example of an analysis ofensemble data obtained by applying PF to a numerical
weather prediction model of the Japan Meteorological
Agency (JMA). It was found that a non-Gaussian shape
was first seen in the probability distribution of the upward vertical wind (W), followed by a change in the shapeof the probability distribution of water vapor (QV) andtemperature (PT).

Genta Ueno

Research Topics
Survey Science NOE

Comparative Analyses with International Survey Data
■ International survey and current issues
One of important research themes of international
survey is comparative analyses of national characters. It
contributes to development of society by clarifying thedifferences in national character brought about by differences in political systems, levels of economic development, and cultural factors such as race, ethnicity, andreligion, and also by finding universal characteristics thatare not affected by differences in these social factors.
On the international survey, face-to-face survey methods have been used for collecting data correctively but
the survey methods have been diversified and the budget required has been decreased by spread of the internet, SNS, and crowdsourcing. However, the change has
also strengthened the concept of privacy protection andlegal problems in academic research have been increased:collecting data online, transferring data across borders,data sharing, and so on.

to live happily in a society with few human relationships
and it can be argued that the government and companies have to construct the welfare systems which canincrease people’s human relationships.
Our researches aim to contribute to the formation ofa better society by examining the values of people in various societies.

Kiyohisa Shibai■ Variables for living happily
It is an important task to find variables to live happily
to every nation and society. It has long been argued frompost-war data that income is positively correlated with aperson's happiness. In 21st century, social capital is considered as one of the important elements for the happiness. In brief, the social capital means richness of good
human relationships (family, friend, and community) anddata of qualifying them as a person’s capital.
7 East Asian countries’ survey data showed that many
people in the Southeast Asia felt happy and, in contrary,people in the Northeast Asia were not so happy (Fig 1).
The result clearly indicates people in developing countries are happier than people in developed ones and only
economic wealth does not necessarily decide people’s
happiness (Fig 2). The data also showed that satisfaction with family was strongly positively corelated to thehappiness (Table 1) and Japan, Korea, and Taiwan had
low happiness scores and low satisfaction with families(Fig 3).
The data analyses indicate that it is difficult for people

Figure 1: Ratio of happiness score.

Figure 2: Dot plot of gdp per capita and happiness score.

How happy currently
Satisfaction with Family finances(income, expenses).629

Employment and job stability

.518

Job

.594

Family life

.716

Married life

.639

Relationships with friends and acquaintances.595

Table 1: Correlation matrix between happiness score andsatisfactions.

Figure 3: Ratio of satisfaction with family.

13

NOE ( Network Of Excellence ) Project
Building a Framework for Advancing Strategic Research andPursuing a New Approach to Collaborative Research■ Research and Educational Activities asa Biaxial Structure

The Institute of Statistical Mathematics (ISM) pursues
research and education in the own biaxial structure; oneis structured by three basic research departments andthe other is by four NOE-research centers and one schoolfor professional development. By its nature, the basic research departments cut across and link various disciplines, with the goal of developing tools for interdisciplinary research. The field of statistical mathematics must
itself evolve to meet the changing needs of society andthe data environment, and is therefore constantly evolving as a field of study. At the same time, there are approaches and directions that have remained unchanged
as the field evolves. There are three basic research departments: Statistical Modeling, Statistical Data Science,and Statistical Inference and Mathematics. Almost all tenured research staff in ISM are assigned in principle to oneof these basic research departments. These departments
engage in cutting edge research aimed at developing
methodologies for rational prediction and decision making, based on data and existing knowledge. We regularly
assess whether our research system is functioning effectively from the viewpoints of research trends and prospects in statistical mathematics.
On the other hand, the NOE-type research centers andthe school (along the vertical axis) are staffed by permanent researchers within ISM, project professors/researchers (post-doctoral researchers), and visiting professors
and researchers. There are four NOE-type research cen-ters: the Risk Analysis Research Center (RARC), the Research Center for Statistical Machine Learning, the Data
Science Center for Creative Design and Manufacturing(DSCCDM), and the Research Center for Medical and
Health Data Science (RCMHDS). These centers conduct
research activities that interface statistical mathematics
with individual scientific disciplines in order to find solutions to urgent social problems. And the School of Statistical Thinking is for professional development. Pleaserefer to the page “Project for Fostering and Promoting
Statistical Thinking” for more details.

■ History of the NOE ProjectIn accordance with the second medium-term plan for
Research Organization of Information and Systems (ROIS,
ISM’s parent organization), ISM had set as a goal the establishment of NOEs (Networks Of Excellence) in statistical mathematics. Initially, ISM established NOEs in Risk
Research, Next-Generation Simulation, Survey Science,
Statistical Machine Learning, and Service Science. Overthe next few years, however, ISM reorganized the domains of NOEs or NOE-type Research Centers considering the needs of each community as well as modern society as a whole. We closed the Survey Science Center in
FY 2016. Its projects were transferred to one of the centers in the Joint Support Center for Data Science (DS) of
ROIS. Survey Science Group at the Dept. of Statistical
Data Science, which is now the core organization, is responsible for connecting and establishing the Survey
Science NOE. The Service Science Research Center was*included in the Risk Analysis Research Center

Fig. 1: Brief history of NOE (Network Of Excellence) Project ( dotted lines mean history or transformation in each NOE )14

ISM Projects

also closed in FY 2016, and its projects were distributed
to other centers according to their methodologies. In
July 2017, ISM launched DSCCDM, and also establishedthe Data Science for Creative Design and Manufacturing
NOE, which inherited a part of the Service Science NOE.
RCMHDS was set up in April 2018. The center, which waslaunched from one of the projects of RARC, is promoting
research and educational activities in medical and healthdata science domains based on the knowledge and networks that ISM obtained in statistical mathematics both
inside and outside Japan (Fig. 1). The Research and Development Center for Data Assimilation was also closed
at the end of FY 2018. Each project has been transferred
to another center in DS, like the Survey Science Center.
The Data Assimilation Group at the Dept. of Statistical
Modeling in ISM has inherited the establishment of the
Next-Generation Simulation NOE. ISM also set up the
new NOE domain and its research unit, Data Science for
Resource-Environment Management in April 2020. As ofthis FY, ISM is promoting projects with seven NOE domains: Risk Research, Next-Generation Simulation, Survey Science, Statistical Machine Learning, Data Science
for Creative Design and Manufacturing, Medical and
Health Data Science, and Data Science for Resource-Environment Management (Fig. 2).

■ Future Perspective of NOE Activities
This NOE Project is a core theme of ISM. To fulfill thegoal of establishing new scientific methodologies in aknowledge-based society, in which the importance ofknowledge goes beyond merely solving individual problems, the NOE activities are being systematically pursued under the unified guidelines formulated by the Managing Committee of the NOE Project. We also commission

experts from the industrial, academic, and government
sectors to be members of the Advisory Board of the NOE
Project, and their advice helps us to promote the projectmuch more effectively. The 3rd Advisory Board Meetingof the NOE Project was held on December 17, 2019. All six
members of the board in each research domain attended
the meeting and engaged in discussion with core members of the project at ISM.
On the basis of this project, ISM, as an Inter-University
Research Institute, will be providing the industrial, academic, and government communities with further opportunities for joint usage (of facilities) and joint research.
This project, made possible by ISM’s special focus in the
cross-disciplinary field of statistical mathematics, is attracting strong support from each of these communities.
As described above, each NOE-type research center
serves as a core hub in its respective fields. The numberof ISM’s memoranda of understanding with research organizations within Japan and overseas is increasing eachyear, including many that span multiple research fields.
ISM’s general research in statistical mathematics, whichis in demand by various research fields in both the humanities and sciences, could connect existing fields
and create new research fields. Hence, ISM plans for the
NOEs to become broader, not only in statistical mathematics but also in data science. ISM is continuously expanding and developing this NOE project with the aim ofestablishing new scientific methodologies (“Fourth Paradigm: Data Science”), creating new research disciplines,
and developing new styles of joint research. For up-todate information, please visit the website. We truly appreciate your understanding and support for this project.

https://www.ism.ac.jp/noe/project/en/

Fig. 2: Relationship diagram of the NOE ( Network Of Excellence) Project.

15

Project for Fostering and Promoting Statistical Thinking

Rapid development of information and communication technology has led to the explosion of data. Now surrounded by “Big Data”, everybody is expected to “think statistically”. More than ever, there is a need for data
scientists who can handle such big data and are able to extract useful knowledge from it. Meanwhile, Japanesehigher education is exhibiting a deplorable lack of production capacity in terms of data scientists. This can be accounted for by the fact that, until quite recently, no academic institution other than ISM had a Ph. D. course in statistics and the small number of statisticians in academia are isolated from each other, being scattered over various disciplines. Hoping to gain a little traction on this problem, ISM established the School of Statistical Thinking,into which we integrated all of our educational resources. In FY 2016, ISM established the Managing Committeeof School of Statistical Thinking, inviting contributions from outside experts, and in FY 2017 we launched the
Leading DAT program by adopting the suggestions by the committee. In FY 2020, we launched online courses.
The following are the principal projects.

Research Collaboration Startup
The Institute had already been providing a consultation service for statistical science, but along withthe launch of the School of Statistical Thinking inNovember 2011, this service was reorganized as a
research collaboration startup. This program, being
one of the projects to foster and promote statisticalthinking, is mainly aimed at supporting applied scientists and other non-experts. Expert statisticians
affiliated with the Institute give them advice on statistical modeling, data analysis, and research. Somecases have developed into official research collaborations, which are our primary duty as an inter-university research institute. The Institute accepts around30 cases annually, some of which benefit society indiverse ways.

Data Science Research Plaza
Researchers funded by private-sector firms canmaintain a desk and phone in the School of Statistical Thinking. This program is subject to fees, and thecontract can be renewed annually. A faculty mentor
gives advice to the accepted funded researcher sothat he or she can freely attend various events, suchas seminars, workshops, conferences, and extension
courses. After learning the expertise of the ISM research staff, participants in this program are invited
to take advantage of paid consultations and funded
research collaboration.

Open-type Professional Development ProgramThis is a spin-out program from ISM cooperative
research projects. Establishing a goal is an indispensable element of the proposal of a cooperative research project. On the other hand, such goal setting
is irrelevant for a summer school program, study session, or retreat. Since the launch of the School of Statistical Thinking, organizers of such group-oriented
16

study programs can apply to the Open-type Professional Development Program. There are two categories under this program: one is ‘workshop’ and theother is ‘intensive training for young researchers’. For
FY 2020, seven workshops have been accepted after
review.

ISM Projects

Statistical Mathematics Seminar Series
The Institute holds weekly seminar series on statistical mathematics every Wednesday. The seminars
are led by in-house and external lecturers to showcase their latest studies. These lectures are currently held online due to COVID-19. To view the seminarschedule and learn more about the program, please
visit the Institute of Statistical Mathematics website.
https://www.ism.ac.jp/index_e.html

Leading DATIn FY 2017, the School of Statistical Thinking
launch­ed a program called “Leading DAT” aimed attraining data scientists with the knowledge and skills
in statistical mathematics required by modern society. In FY 2020, we held two Leading DAT lectures
online, entitled "L-S. Decision Trees and Ensemble
Learning: From Basics to Practice" and “L-B1. An Introduction to Statistical Modeling”. Around one thirdof the attendants of L-B1 enroll the Leading DAT
Training Course (half course), in which we grant certificates to participants who have fulfilled the course
requirements, including attendance in L-B1 lectureand submission of reports. A total of 20 people havebeen granted the certificate of completion.

Online lecture ( L-B2)

Tutorial Courses
The education program at ISM dates back to 1944,
the year of founding. The Ministry of Education installed a training center within ISM to foster technicians in numerical computation. After the World War
II, this training center was relaunched in 1947 to develop pollsters and census takers. It helped to cultivate professionals in the field of statistical surveys,
while a growing number of entries from business andindustry coming for various types of training werealso observed.
Now the tutorial courses are operated by the

School of Statistical Thinking, which was establishedin 2011.
In FY 2020, 4 lectures and one course (including
Leading DAT) were held and the number of participants was 321. The total numbers of lectures andcourses held from 1969 to March, 2021 were 386 and4 respectively, with a total of 28,220 participants.
These lectures covered a wide range of fields frombasic to applied statistics. The schedule of tutorial
courses can be found on the website of the Instituteof Statistical Mathe­matics.
https://www.ism.ac.jp/lectures/kouza.html
17

Research Cooperation

International Cooperation■ Associated Foreign Research Institutes
Organization name

Address

Conclusion day

The Statistical Research Division of the U.S. Bureau of the Census
Stichting Mathematisch Centrum
Institute for Statistics and Econometrics, Humboldt University of Berlin
The Steklov Mathematical InstituteCentral South University
Soongsil University
University of Warwick
Indian Statistical Institute
Institute of Statistical Science, Academia Sinica
Department of Empirical Inference,
Max Planck Institute for Biological Cybernetics
Department of Communication Systems,
SINTEF Information and Communication Technology
University College London
Department of Electronics and Telecommunications,
Norwegian University of Science and Technology
Department of Probability and Mathematical Statistics,
Charles University in Prague
Department of Ecoinformatics, Biometrics and Forest Growth of the
Georg-August University of Goettingen
Korean Statistical Society (KSS )USA ( Washington )July 27, 1988

Toyota Technological Institute at Chicago
Australian National University
RiskLab ETH Zurich
Institut de Recherche en Composants logiciel et matériel pourl’Information et la Communication Avancee
Le laboratoire de mathématiques de I’Universite Blaise Pascal
Centre de Rechereche en Informatique, Signal et Automatique de Lille
University College London Big Data Institute
The Institute of Forestry, Pokhara of Tribhuvan University
The Institute of Forest and Wildlife Research and Developmentof the Forestry Administration of Cambodia
The Chancellor masters and Scholars of the University of Oxford
Forest Inventory and Planning Institute of Vietnam
Zuse Institute Berlin
The University of Porto
National University of Laos
Institute of Geophysics China Earthquake Administration
Hong Kong Baptist University
University of Malaya
Unversidade de Évola
Universität Ulm
The Korean Association for Survey Research
The Jean Golding Institute for data-intensive research, University of Bristol
Survey Research Center, Sungkyunkwan University
University of Lampung
Department of Earth and Space Sciences, Southern University of
Science and Technology
Université Bretagne Sud
North Carolina State University
National University of Singapore
Konrad-Zuse-Zentrum für Informationstechnik Berlin
Singapore-ETH Centre
Department of Actuarial Studies and Business Analytics, Macquarie University* There are two more agreements concluded.

18

The Kingdom of the Netherlands ( Amsterdam ) May 10, 1989
Germany ( Berlin )December 8, 2004

Russia ( Moscow )August 9, 2005

China ( Changsha )November 18, 2005

The Republic of Korea ( Seoul )April 27, 2006

The United Kingdom ( Coventry )January 16, 2007

India ( Kolkata )October 11, 2007

Taiwan ( Taipei )June 19, 2008

Germany ( Tubingen )August 11, 2010

Norway ( Trondheim )January 30, 2012

The United Kingdom ( London )February 16, 2012

Norway ( Trondheim )May 22, 2012

Czech Republic (Prague )October 10, 2012

Germany (Goettingen )October 18, 2012

The Republic of Korea ( Seoul )July 9, 2013

USA ( Chicago )February 10, 2014

Australia ( Canberra )May 15, 2014

Switzerland ( Zurich )February 7, 2015

France ( Paris )February 9, 2015

France (Clermont-Ferrand )February 11, 2015

France ( Paris )February 12, 2015

The United Kingdom ( London )February 26, 2015

Nepal ( Pokhara )March 6, 2015

Cambodia ( Phnom Penh )March 6, 2015

The United Kingdom ( Oxford )March 10, 2015

Vietnam ( Hanoi )June 2, 2015

Germany (Berlin )June 20, 2016

Portugal ( Porto )June 22, 2016

Laos ( Vientiane )March 15, 2017

China ( Beijing )April 28, 2017

Hong Kong ( Kowloon Tong )August 28, 2017

Malaysia ( Kuala Lumpur)

September 18, 2017

Portugal ( Evola )November 30, 2017

Germany ( Ulm )December 8, 2017

The Republic of Korea ( Seoul )February 14, 2018

The United Kingdom ( Bristol)January 15, 2019

The Republic of Korea ( Seoul )February 25, 2019

Indonesia ( Lampung )March 6, 2019

China ( Shenzhen Shi )March 25, 2019

France ( Lorient )March 29, 2019

USA ( Raleigh )November 13, 2019

Singapore
Germany ( Berlin)

February 25, 2020

Singapore

March 18, 2020

Australia (Sydney)

December 21, 2020

Research Collaboration
ISM performs many activities for collaborating with researchers in the various fields of statistical science,from the individual level to the national level. The ISM cooperative research program regularly performs research activities to provide the research resources of ISM to researchers at universities or research institutes inorder to advance their academic research. Available research resources include books, journals, supercomputers, some commercial statistical software packages, as well as statistical packages developed by ISM, and also
the researchers in ISM themselves, who have abundant professional knowledge and experience in statisticalscience and data analysis. The ISM cooperative research program provides not only research support funds
but also opportunities for the various researchers in many fields who require statistical knowledge to make useof the resources available at ISM. ISM’s aim is to be a place for interaction and fusion among researchers insideand outside of ISM, and to contribute to multidisciplinary development of both the theory and the applicationof statistical science.

■ Number of Activities
Year

2015

2016

2017

2018

2019

2020

Number of Activities

183

187

161

166

178

145

■ Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below to findthe most appropriate type of project.
ISM Fields
NumberFieldsNumberFieldsa

Prediction and Control Groupf

Structure Exploration Groupb

Complex System Modeling Groupg

Mathematical Statistics Groupc

Data Assimilation Grouph

Learning and Inference Groupd

Survey Science Groupi

Mathematical Optimization Groupe

Metric Science Groupj

OthersMajor Research Fields
NumberFieldsMajor Research Domains

1

Statistical mathematics

Mathematical theory of statistics, optimization, etc.

2Information science

Algorithms, use of computer in statistics, etc.

3

Biological science

Medicine, pharmacy, epidemiology, genetics, etc.

4Physical science

Space, planet, earth, polar region, materials, etc.

5Engineering

Mechanics, electronics, control, chemistry, architecture, etc.

6

Human science

Philosophy, art, psychology, education, history, geography, culture, language, etc.

7Social science

Economics, law, politics, society, manage­ment, official statistics, population, etc.

8

Environmental science

Environmental statistics, environmentrics, agricultural statistics, statistical meteorology,
land economics, landscape management, forest management, etc.

9

OthersOther research fields

19Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of SOKENDAI (The Graduate University for
Advanced Studies; the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate education. Since its opening, the Institute has included the Department of Statistical Science and, sinceApril 1989, has accepted students for education and research in doctoral programs. In 2006, the Instituteadopted a five-year system, offering either a five-year education and research program, or a three-year education and research program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science

SOKENDAI

Accelerator Science

School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics

School of Life Science

Basic Biology
Physiological Sciences

School of Advanced Sciences

Evolutionary Studies of Biosystems

Outline of Education and Research
The Department of Statistical Science, which is based on the Institute of Statistical Mathematics (ISM) serving
as its underlying platform, aims to cultivate individuals who possess creative research skills to contribute tosolving various important intricately-intertwined problems. To this end, the Department conducts education
and research related to the basis, mathematics and applications of data collection designs, modeling, inference
and prediction, and equip students with the ability to extract information and knowledge from the real world
based on the effective use of data.
Field of Education and Research

20

Contents

Statistical Modeling

We provide education and research focused on dynamic modeling such as spatial and space-time modeling, graphical modeling of temporally and/or spatially inter-related complex phenomena, and intelligent
information processing. We also provide education and research on statistical inference based on various
models, methods of calculation for inference, and evaluation of models based on data.

Statistical Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Statistical Inference
and Mathematics

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

Features of Education and Research● The course is the only integrated doctoral program on statistical science in Japan. It has received students

from a wide variety of disciplines and has offered education and research on statistical science by professors
specialized in many different fields, from theory through to practical applications.

● The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, as well
as a variety of software, including original statistical software developed by the Institute.

● The academic publications and books on statistical and mathematical sciences produced are some of the bestin the world.

● In its role as an inter-university research institute, the Institute holds frequent workshops and seminars byvisiting professors and researchers from both Japan and abroad. Students are free to attend and participate.

● It is possible to collaborate with researchers from other universities and institutions. It is also possible forstudents to develop their own projects by participating in research projects with other institutions.

Course Requirements and Type of Degree Granted● Requirements to complete the doctoral course are as follows:

Completion of at least 40 credits, including the required ones, by a student in the five-year program, or completion of at least 10 credits by a three-year doctorate student who previously completed a Master’s course;
meeting all the criteria set by the thesis committee of the Institute; and successfully completing the examination.

● On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly with aninter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.

● The required number of years of study will be flexible if a student demonstrates outstanding research results.

Number of Students ( As of April 1, 2021)■ 5-year doctoral

course:Quota,2■ 3-year doctoral

course:Quota,3Year of enrollment

2016

2017

2018

2019

2021

Number of students

1

1

1

2

1

Year of enrollment

2016

2017

2018

2019

2020

2021

Number of students

4③

3③

7⑤

15 ⑫

3①

3①

* The figures in circles indicate those who are employed by other organizations.

University Background of Students
National and public universities• Hokkaido University ( 5 ) • Tohoku University (4 ) • Fukushima University (1) • University of Tsukuba (7 ) • Saitama University (1)• Chiba University (1) • Ochanomizu University (1) • National Graduate Institute for Policy Studies (1) • Tokyo Medical and Dental
University (1) • Tokyo University of Marine Science and Technology (1) • Tokyo Gakugei University (2) •Tokyo Institute of Technology ( 6 )• The University of Tokyo (23 ) • Tokyo Metropolitan University (1) • Tokyo University of Agriculture and Technology (1) • Hitotsubashi
University (6 ) • Shizuoka University (1) • Kanazawa University (1) • Japan Advanced Institute of Science and Technology (1) • Nagoya
University (4 ) • Toyohashi University of Technology ( 2 ) • Kyoto University ( 7 ) • Osaka City University (1) • Osaka University ( 3 )• Nara Institute of Science and Technology (1) • Okayama University ( 2 ) • Shimane University (3 ) • Kyushu University ( 4 ) • Oita
University (1) • The University of Electro-Communications ( 1)21Graduate School Program

University Background of Students
Private universities• Aoyama Gakuin University (1 ) • Kitasato University (1 ) • Keio University (8 ) • International Christian University (1) • Shibaura Institute ofTechnology (1 ) • Sophia University (1 ) • Chuo University (9 ) • Tokyo University of Science (7 ) • Toyo University (1) • Japan Women’s
University (1 ) • Nihon University ( 2 ) • Hosei University (7 ) • Waseda University (9 ) • Nanzan University (1 ) • Osaka Electro-Communication
University ( 1 ) • Kansai University ( 1 ) • Kyoto Sangyo University (1 ) • Ritsumeikan University (1 ) • Okayama University of Science (1 )• Kurume University (1 )Foreign universities• Aston University (1) • University of California, Irvine (1) • California State University, Long Beach (1) • University of Campinas (1)• University of Colorado Boulder (2) • University of Dhaka (2 ) • University of Hawaii (1) • Jahangirnagar University (2 ) • University of
Malaya (1) • Northeast Normal University (1) • Ohio University ( 2) • University of Rajshahi (2 ) • Stanford University (1) • The University of
Nottingham (1 ) • Zhejiang University (1 ) • Institute of Applied Mathematics, AMSS, CAS (1 ) • University of Science and Technology ofChina (1 ) • Center for Analysis and Prediction, China Seismological Bureau (1 ) • Northeastern University (1 ) • The Hong Kong Universityof Science and Technology (1 ) • China University of Geosciences (1 )Degrees AwardedYear
Doctor of Philosophy

2014

2015

2016

2017

2018

2019

2020

5

5

7

5

5

5

4

AlumniNational and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine • University of Tsukuba • University of Hyogo • The University of Tokyo• The University of Electro-Communications • Saitama University • Nagoya University • Kyushu University • Kyushu Institute of Technology• University of the Ryukyus • The Institute of Statistical Mathematics • Tohoku University • Yokohama National University • Hokkaido
University • Tokyo Institute of Technology • Hiroshima University • Oita University of Nursing and Health Sciences • JAXA’s Engineering
Digital Innovation Center • Kyoto University • Nara Institute of Science and Technology • Bank of Japan • Japan Broadcasting Corporation• Railway Technical Research Institute • Statistical Information Institute for Consulting and Analysis • Government Pension Investment Fund• Public School • RIKEN • Statistics Bureau of Japan • Pharmaceuticals and Medical Devices Agency (PMDA)
Private universities• Sapporo Gakuin University • Tokyo Health Care University • Meiji University • Doshisha University • Josai University • Nihon University• Komazawa University • Aichi University of Technology • Tokyo University of Information Sciences • Shibaura Institute of Technology• Rikkyo University • Waseda University • Keio University
Foreign universities• Jahangirnagar University • Victoria University • Massey University • University of Otaga • Statistics New Zealand • University of
Rajshahi • University of California, Los Angeles • Asia-Pacific Center for Security Studies Department • Central South University • Hong
Kong Baptist University • University of South Carolina • The University of Warwick
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory • NTT Communication Science Laboratories • Seiwa Kikaku • NLI Research Institute • Mizuho
Trust and Banking • Nomura Securities Co., Ltd. • ATR Computational Neuroscience Laboratories • Toyota Motor Corporation, Higashi-Fuji
Technical Center • Schlumberger Limited • Macquarie Securities, Japan • Non-Life Insurance Rating Organization of Japan • Barclays
Global Investors • Open Technologies Corporation • Yamaha Corporation • Goldman Sachs Asset Management L.P. • CLC bio Japan, Inc.
• MUFG Bank, Ltd. • Pfizer Japan Inc. • Doctoral Institute for Evidence Based Policy • Sony Corporation • NTTIT Corporation • Sompo
Japan Insurance Inc. • Qualicaps Co.,Ltd. • Bridgestone Corporation • Brain Pad Inc. • Sumitomo Chemical Co.,Ltd.
• PricewaterhouseCoopers Aarata • Mitsubishi Tanabe Pharma Corporation • Daiichi Sankyo Co.,Ltd. • Shizuoka Cancer Center • CPC
Clinical Trial Hospital, Medipolis Medical Research Institute • CRD Association • Japan Society for the Promotion of Science • Tokyo
Electric Power Company Holdings, Inc. • Asahi Kasei Corporation • Honda R&D Co.,Ltd. • Yokogawa Electric Corporation • Kao
Corporation • Advanced Smart Mobility Co., Ltd. • NEC Corporation • Janssen Pharmaceutical K.K. • Taisho Pharmaceutical Holdings22Facilities and Equipment

Computational Resources ( As of April 1, 2021)
ISM is operating a supercomputer system, the Supercomputer System for Statistical Science, which is suitable for analyzing large-scale datasets. The Supercomputer System for Statistical Science, an HPE SGI 8600 system,has been operated since October 2018. The system is a
distributed-memory parallel computer that has total theoretical peak performance of 1.49 petaflops. The system
is liquid cooled and consists of 384 compute nodes. Eachnode has two CPU chips (Intel Xeon GOLD 6154) with 18
cores and has 384 GB memory. The system includes hardware random number generator. For visual representation of research results, ISM has a 200-inch wide screen
and a projector capable of showing 3D movies in 4K resolution in the historical computers exhibit room.
In March 2021, we introduced the Communal Cloud
Computing System to provide a computing environmentthat is easy for each user to use and customize. This sys-

tem is equipped with 64 computing nodes (HPE ProLiant
DL385 Gen 10 Plus; total theoretical computing performance of 154.0TFlops), and each node has two 32-coreCPUs (AMD EPYC 7452), 1 TB of main memory, and an
SSD with 20 TB of usable capacity.
In the main office building, the primary local area
network (LAN) consists of an Ethernet network using
10GBase-SR for the main trunk and 1000Base-T for branches. The personal computers in researchers’ offices and thesupercomputer system are all connected to this network.
A wireless LAN system is also available in the immediatearea of the building occupied by ISM. These LAN systems
enable distributed processing and allow computing resources and statistical data to be used effectively. Comprehensive network security methods have been implemented, such as a firewall system, anti-virus software,and an intrusion prevention system. To encourage joint
research with researchers both in Japan and abroad, as
well as the exchange of e-mails, the network is connectedto the Internet through SINET5 (40 Gbps).

Supercomputer System for Statistical Science ( HPE SGI 8600 )

200-inch wide screen showing 3D movies in 4K resolution

Library and Materials ( As of April 1, 2021)
We have a large number of major Japanese/foreign
journals covering a wide variety of fields including statistics, mathematics, computer science and informatics. In
addition, we also have a large library consisting of books
on humanities, social science, biology, medical science,science and engineering.
Besides contributed to Japanese and foreign pub­lications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Sta­tistical
Mathematics (English; Springer), Proceedings of the Institute of Statistical Mathematics (Japanese), ISM Survey
Research Report (Statistical Researches mainly related
to the Japanese National Character), Computer Science
Monographs, Cooperative Research Re­ports (for collaborative research projects), Research Memorandum, ISM

Reports on Statistical Computing, and ISM Report on
Research and Education.
All materials are properly catalogued and can besearched from the web in order to meet the needs ofresearch­ers working in a wide of fields. We also accept
photocopy requests.

Japanese
books
20,35328%

Books
73,424

Japanese
periodicals
1,163
53%

Foreignperiodicals

Periodicals
2,197

1,034
47%

Foreign books
53,071
72%

23

Finance and Buildings

Administration Subsidy and Others ( 2020)
Type

Personnel expenses

Non-personnel expenses

Total

653,933

864,450

1,518,383

Expenditure

Unit: 1,000JPY

Accepted External Funds ( 2020)
Type

Joint
research

Subcontracted
research,
Trustee businessAcademic
Consulting

Contract
researchers

Contribution for
scholarship

Total

Items

24

22

7

2

3

58

Income46,450120,778

5,114

566

5,600

178,508
Unit: 1,000JPY

Grant-in-Aid for Scientific Research “ KAKENHI” ( 2020)
Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research on Innovation Areas

2

21,710Grant-in-Aid for Scientific Research ( S )——Grant-in-Aid for Scientific Research ( A)

6

46,670Grant-in-Aid for Scientific Research ( B)827,834

Grant-in-Aid for Scientific Research ( C)19

25,090Grant-in-Aid for Challenging Research ( Exploratory )

2

3,640

Grant-in-Aid for Early-Career Scientists

13

16,429Grant-in-Aid for Research Activity Start-up——

Grant-in-Aid for JSPS Fellows

1

780

Grant-in-Aid for Publication of Scientific Research Results

—

—

51

Total

51

142,153
Unit: 1,000JPY

Site and Buildings ( As of April 1, 2021)

24

Site Area

62,450m 2

Area for Buildings (total)

16,209m 2

Organization

Organization Diagram ( As of April 1, 2021)
International
Advisory Board

Department of Statistical
Modeling

Council

Department of Statistical
Data Science

Cooperative ResearchCommittee
Advisory Board of
NOE ProjectManaging Committee of
School of Statistical Thinking
Research Ethics Review
Committee
DirectorGeneral

Department of Statistical
Inference and Mathematics■ Prediction and Control Group■ Complex System Modeling Group■ Data Assimilation Group■ Survey Science Group■ Metric Science Group■ Structure Exploration Group■ Mathematical Statistics Group■ Learning and Inference Group■ Mathematical Optimization Group

Risk Analysis Research Center
Research Center for
Statistical Machine Learning
Data Science Center for Creative
Design and Manufacturing
Research Center for Medical and
Health Data Science
School of Statistical Thinking

Vice DirectorGeneral

Center for Engineering andTechnical Support
Library
Planning Unit

■ Computing Facilities Unit
■ Computer Networking Unit
■ Information Resources Unit
■ Media Development Unit

■ URA Station

Evaluation Unit
Information and Public Relations Unit
Administration Planningand Coordination Section

Industry-Academia Collaboration and Intellectual Property Unit
NOE Promotion Unit
Gender Equality Unit
International Affairs Unit
General Affairs Division
Financial Division

Tachikawa Administration
Department

Accounting Division
Research Promotion Division
NIPR Planning and Coordination Division
ISM Planning and Coordination Division25

Number of Staff ( As of April 1, 2021)
DirectorGeneral

Type

Director-General

Assistant Administ­ Technical
Professor Associate
Staff
Professor Professor rative Staff

Total

1

1

Department of Statistical Modeling66Department of Statistical Data Science

56Department of Statistical Inference and Mathematics

7

8

12
2

13
15

School of Statistical Thinking

22Center for Engineering and Technical Support
Administration Planning and Coordination Section

10( 36 )

(1 )

( 37)

1 ( 36 )

10 (1 )

54( 37 )1Tachikawa Administration Department
Total

10

1

18

20
(

4

1

) Total number of staff of Tachikawa Administration Department.

The number under Technical Staff at the Center for Engineering and Technical Support and Administrative Staff at the Tachikawa Administration Department
include two each staff member who retired because of age but was reemployed in a different position.

Staff ( As of August 1, 2021)
Director-General

Hiroe TSUBAKI

Vice Director-General

Yoshinori KAWASAKI

Vice Director-General

Satoshi YAMASHITA

Vice Director-General

Yoshihiko MIYASATO

Department of Statistical Modeling
Director

Tomoko MATSUI• Prediction and Control Group ————————————————————————————————————————————————————————————
Prof.

Yoshinori KAWASAKIProf.

Yoshihiko MIYASATO

Prof.

Atsushi YOSHIMOTO

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Fumikazu MIWAKEICHI• Complex System Modeling Group ——————————————————————————————————————————————————————————Prof.

Tomoko MATSUIProf.

Yukito IBA

Assoc. Prof.

Shinsuke KOYAMA

Assoc. Prof.

Kengo KAMATANI

Prof.

Hideitsu HINO

• Data Assimilation Group ———————————————————————————————————————————————————————————————Prof.

Genta UENO

Project. Prof.

Shigeru FUJITA

Assoc. Prof.

Shinya NAKANO

Project Researcher

Yusaku OHKUBO

Visiting Prof.

Masako KAMIYAMA

Visiting Prof.

Toshikazu KITANO

Visiting Prof.

Tadahiko SATO

Visiting Prof.

Kazuyuki NAKAMURA

Visiting Prof.

Tomoyuki HIGUCHI

Visiting Assoc. Prof. Hiroshi KATO

Visiting Assoc. Prof. Masaya SAITO

Visiting Assoc. Prof. Shunichi NOMURA

Visiting Assoc. Prof. Yosuke FUJII

Visiting Assoc. Prof. Hiromichi NAGAO

Department of Statistical Data Science
Director

Koji KANEFUJI• Survey Science Group —————————————————————————————————————————————————————————————————————
Prof.

Kazuhiro MINAMI

Project Assoc. Prof. Naoko KATO
Visiting Prof.
Visiting Prof.

Takatoshi IMADA
Wataru MATSUMOTO

Visiting Assoc. Prof. Koken OZAKI

26

Assoc. Prof.

Tadahiko MAEDA

Project Assist. Prof. Kiyohisa SHIBAIVisiting Prof.
Visiting Prof.

Toru KIKKAWA
Kazufumi MANABE

Visiting Assoc. Prof. Taisuke FUJITA

Assoc. Prof.

Yoo Sung PARK

Project Assist. Prof. Le Duc ANH
Visiting Prof.
Visiting Prof.

Yoshimichi SATO
Masahiro MIZUTA

Visiting Assoc. Prof. Yusuke INAGAKIOrganization

Staff
Department of Statistical Data Science• Metric Science Group —————————————————————————————————————————————————————————————————
Prof.

Satoshi YAMASHITA

Prof.

Koji KANEFUJI

Prof.

Shigeyuki MATSUIAssoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Hisashi NOMA

Assist. Prof.

Nobuo SHIMIZU

Project Researcher

Hiroka HAMADA

Project Researcher

Yasuhiro TANAKA• Structure Exploration Group —————————————————————————————————————————————————————————————Prof.

Ryo YOSHIDA

Assoc. Prof.

Jun ADACHI

Assoc. Prof.

Stephen WU

Assist. Prof.

Daisuke MURAKAMI

Assoc. Prof.

Kenichiro SHIMATANI

Department of Statistical Inference and Mathematics
Director

Satoshi ITO• Mathematical Statistics Group ———————————————————————————————————————————————————————————————
Prof.

Satoshi KURIKI

Prof.

Yoshiyuki NINOMIYAProf.

Shuhei MANO

Assoc. Prof.

Shogo KATO

Assoc. Prof.

Takaaki SHIMURA

Assoc. Prof.

Keisuke YANO

Visiting Prof.

Akimichi TAKEMURA

Visiting Prof.

Nobuaki HOSHINO• Learning and Inference Group —————————————————————————————————————————————————————————————
Prof.

Kenji FUKUMIZU

Prof.

Hironori FUJISAWA

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Ayaka SAKATA

Assoc. Prof.

Daichi MOCHIHASHI• Mathematical Optimization Group —————————————————————————————————————————————————————————
Prof.

Satoshi ITO

Assoc. Prof.

Figueira LOURENÇO BRUNO

Prof.

Shiro IKEDA

Assoc. Prof.

Mirai TANAKA

Risk Analysis Research Center
Director

Satoshi YAMASHITA

Vice Director

Shogo KATO

Prof.

Satoshi YAMASHITA

Prof.

Satoshi KURIKI

Prof.

Koji KANEFUJI

Prof.

Tomoko MATSUIProf.

Atsushi YOSHIMOTO

Prof.

Yoshinori KAWASAKIProf.

Yoshiyuki NINOMIYAProf.

Kazuhiro MINAMI

Prof.

Shuhei MANO

Assoc. Prof.

Assoc. Prof.
Assoc. Prof.

Keisuke YANO
Kenichiro SHIMATANI

Assoc. Prof.

Assoc. Prof.

Kengo KAMATANI
Jiancang ZHUANG

Masayuki HENMI
Shogo KATO

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Takaaki SHIMURA

Assoc. Prof.

Project Assoc. Prof. Masayuki KUMON
Project Assis. Prof.

Takaaki KOIKE

Project Assist. Prof. Hideaki NAGAHATA

Project Assoc. Prof. Yuuki RIKIMARU

Assoc. Prof.

Assist. Prof.

Stephen WU
Daisuke MURAKAMI

Project Assis. Prof.

Xiong ZIYAO

Project Assis. Prof.

Tran DUC VU

Project Researcher

Yosihiko OGATA

Visiting Prof.

Masakazu ANDO

Visiting Prof.

Shinsuke ITO

Visiting Prof.

Masao UEKI

Visiting Prof.

Tadashi ONO

Visiting Prof.

Takashi KAMEYA

Visiting Prof.

Naoto KUNITOMO

Visiting Prof.

Naoki SAKAI

Visiting Prof.

Yo SHEENA

Visiting Prof.

Yasutaka SHIMIZU

Visiting Prof.

Kyomi SHIRAKAWA

Visiting Prof.

Kazuyuki SUZUKI

Visiting Prof.

Rinya TAKAHASHI

Visiting Prof.

Satoshi TAKIZAWA

Visiting Prof.

Hideatsu TSUKAHARA

Visiting Prof.

Hiroshi TSUDA

Visiting Prof.

Tetsuji TONDA

Visiting Prof.

Shunji HASHIMOTO

Visiting Prof.

Hisayuki HARA

Visiting Prof.

Masaaki FUKASAWA

Visiting Prof.

Satoshi FUJII

Visiting Prof.

Toshihiro HORIGUCHI

Visiting Prof.

Toshio HONDA

Visiting Prof.

Mihoko MINAMI

Visiting Prof.

Sadaaki MIYAMOTO

Visiting Prof.

Hitoshi MOTOYAMA

Visiting Prof.

Hirokazu YANAGIHARA

Visiting Prof.

Yoshiki YAMAGATA

Visiting Prof.

Nakahiro YOSHIDA

Visiting Prof.

Takaaki YOSHINO

Visiting Prof.

Toshinao YOSHIBA

Visiting Prof.

Tetsuya IWASA

Visiting Prof.

Katsutoshi NAGASHIMA

Visiting Prof.

Hiroaki NAGAFUJI

Visiting Assoc. Prof. Takaki IWATA

Visiting Assoc. Prof. Bogdan Dumitru ENESCU

Visiting Assoc. Prof. Yukihiko OKADA

Visiting Assoc. Prof. Teppei OGIHARA

27

Risk Analysis Research Center
Visiting Assoc. Prof. Kenichi KAMO

Visiting Assoc. Prof. Takafumi KUBOTA

Visiting Assoc. Prof. Yuta KOIKE

Visiting Assoc. Prof. Masashi KONOSHIMA

Visiting Assoc. Prof. Noriyoshi SAKUMA

Visiting Assoc. Prof. Seisho SATO

Visiting Assoc. Prof. Junichi TAKAHASHI

Visiting Assoc. Prof. Isao TAKABE

Visiting Assoc. Prof. Dou XIAOLING

Visiting Assoc. Prof. Kazuyoshi NANJO

Visiting Assoc. Prof. Keisuke FUKUI

Research Center for Statistical Machine Learning
Director

Kenji FUKUMIZU

Prof.

Kenji FUKUMIZU
Yoshihiko MIYASATO

Prof.

Prof.
Prof.

Vice Director

Tomoko MATSUI
Prof.

Prof.

Tomoko MATSUI
Satoshi ITO

Prof.

Yukito IBA
Shiro IKEDA

Satoshi KURIKI

Prof.

Shuhei MANOProf.

Hironori FUJISAWA

Prof.

Hideitsu HINO
Ayaka SAKATA

Assoc. Prof.

Assoc. Prof.

Kazuhiro MINAMI
Shinsuke KOYAMA

Daichi MOCHIHASHI
Mirai TANAKA

Assist. Prof.

Daisuke MURAKAMI

Project Assist. Prof. Sho SAITO

Project Assist. Prof. Yoichi MOTOTAKE

Project Assist. Prof. Kotaro SAKAMOTO

Project Assist. Prof. Hideto NAKAJIMAProf.

Assoc. Prof.

Project Assist. Prof. Toshimitsu ARITAKE

Assoc. Prof.

Project Researcher

Zheng NING

Visiting. Prof.

Arthur GRETTON

Visiting. Prof.

Masataka GOTO

Visiting. Prof.

Yuji SHINANO

Visiting. Prof.

Takashi TSUCHIYA

Visiting. Prof.

Katsuki FUJISAWA

Visiting Assoc. Prof. Masaaki IMAIZUMIVisiting Assoc. Prof. Shuichi KAWANO

Visiting Assoc. Prof. Kei KOBAYASHI

Visiting Assoc. Prof. Sayaka SHIOTA

Visiting Assoc. Prof. Tsutomu TAKEUCHI

Visiting Assoc. Prof. Eiji MOTOHASHI

Visiting Assoc. Prof. Makoto YAMADA

Data Science Center for Creative Design and Manufacturing
Director

Ryo YOSHIDA

Vice Director

Hironori FUJISAWAProf.

Ryo YOSHIDA

Prof.

Hironori FUJISAWA

Prof.

Kenji FUKUMIZU

Prof.

Hideitsu HINO

Assoc. Prof.

Shinya NAKANOAssoc. Prof.

Stephen WU

Project Assist. Prof. Liu CHANG

Project Assist. Prof. Yoshihiro HAYASHI

Project Assist. Prof. Yuta AOKI

Project Researcher

Aiko TAKAHASHI

Project Researcher

Yoh NOGUCHI

Project Researcher

Visiting. Prof.

Junichiro SHIOMI

Visiting. Prof.

Junko MORIKAWA

Visiting Assoc. Prof. Masaaki TAKADA

Hironao YAMADA

Research Center for Medical and Health Data Science
Director

Shigeyuki MATSUI

Vice Director

Ikuko FUNATOGAWA

Prof.

Satoshi YAMASHITA

Prof.

Shigeyuki MATSUI

Project Prof.

Shinto EGUCHI

Assoc. Prof.

Hisashi NOMA

Assoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Fumikazu MIWAKEICHI

Project Assoc. Prof. Mayumi OKA

Visiting. Prof.

Manabu AKAZAWA

Visiting Prof.

Yoichi ITO

Visiting. Prof.

Senichiro KIKUCHI

Visiting. Prof.

Ken KIYONO

Visiting. Prof.

Atsushi GOTO

Visiting Prof.

Toshiya SATO

Visiting Assoc. Prof. Kunihiko TAKAHASHI

Visiting Prof.

Hisateru TACHIMORI

Visiting Prof.

Satoshi TERAMUKAI

Visiting. Prof.

Satoshi HATTORI

Visiting Prof.

Michiko WATANABE

Visiting Assoc. Prof. Masataka TAGURI
Visiting Prof.

Nobuaki NISHIYAMA

Visiting Assoc. Prof. Ryoichi KIMURA

Visiting Assoc. Prof. Yasunori SATO

Visiting Assoc. Prof. Kazushi MARUO

Visiting Assoc. Prof. Shonosuke SUGASAWA

Visiting Assoc. Prof. Kengo NAGASHIMA

Visiting Assoc. Prof. Chieko ISHIGURO

School of Statistical Thinking
Director

28

Satoshi KURIKI

Vice Director

Yukito IBA

Prof.

Yoshinori KAWASAKIProf.

Yukito IBA

Prof.

Satoshi KURIKI

Prof.

Hironori FUJISAWA

Prof.

Kazuhiro MINAMIProf.

Yoshiyuki NINOMIYAOrganization

Staff
School of Statistical Thinking
Prof.

Hideitsu HINOProf.

Shiro IKEDA

Project Prof.

Masato CHINO

Project Prof.

Hiroko NAKANISHI

Project Prof.

Kazuo MUROTA

Project Prof.

Manabu IWASAKI

Project Prof.

Kunio TANABE

Project Prof.

Yasunori SAWAMURA

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Keisuke YANO

Assist. Prof.

Akifumi OKUNO

Kei NOBA

Project Assist. Prof. Kazuhei KIKUCHI

Assist. Prof.

Visiting Assoc. Prof. Osamu KOMORI

Visiting Assoc. Prof. Kei TAKAHASHI

Center for Engineering and Technical Support
Director
Deputy Manager

Genta UENO
Yuriko WATANABE

Vice Director

Kazuhiro MINAMI

Unit Leader of Computing Facilities Unit

Mitsuru HAYASAKA

Unit Leader of Computer Networking Unit

Kazuhiro NAKAMURA

Unit Leader of Information Resources Unit

Yuriko WATANABE

Unit Leader of Media Development Unit

Akiko NAGASHIMA

Library
Head

Genta UENO

Administration Planning and Coordination Section
Chief Director

Hiroe TSUBAKI

Yoshinori KAWASAKI

Director of Planning Unit

Director of Information and Public Relations Unit Yoshihiko MIYASATO
Director of NOE Promotion Unit

Satoshi YAMASHITA

Director of International Affairs Unit

Hiroe TSUBAKI

Director of Evaluation Unit

Yoshihiko MIYASATO

Director of Industry-Academia Collaboration
and Intellectual Property Unit

Satoshi YAMASHITA

Director of Gender Equality Unit

Yoshinori KAWASAKI• URA Station ——————————————————————————————————————————————————————————————————————

Kozo KITAMURA

Motoi OKAMOTO

Keisuke HONDA

Tachikawa Administration Department
Director Hikaru SASAGAWA• General Affairs Division ————————————————————————————————————————————————————————————————————
Manager Mitsuo HAMADA

Deputy Manager

Hisayuki IGARASHI

Deputy Manager
Head, General Affairs Team

Ayako NARITA

Head, Personnel Team

Head, Labor Management Team

Keiko SHIMIZU

Head, Graduate School Team

Akiharu MIYAGI• Financial Division ———————————————————————————————————————————————————————————————————
Manager

Katsuhiro OZAKI

Deputy Manager

Hiroaki ARAI

Head, General Affairs and Audit Team

Hiroaki ARAI

Head, Budget and Account Settlement Team (NIPR) Takeshi KUWAHARA

Head, Budget and Account Settlement Team (ISM)

Akiko MAEKAWA

Head, Assets Management and Acceptance Team

Taku SASAKI• Accounting Division ————————————————————————————————————————————————————————————————————
Manager

Koji SAKAMOTO

Deputy Manager

Specialist

Hiroshi TAKAGIHead, Accounting Team

Hitoshi HIRAYAMA

Head, Contract Team ( NIPR )Yumiko OKAWA

Head, Contract Team ( ISM )Toshiaki TAKASAKIHead, Facilities Team

Takuya SAITO• Research Promotion Division ————————————————————————————————————————————————————————————————————
ManagerHead, Research Promotion Team

Kumiko TANSHO

Ichiro KAWAJI

Deputy Manager

Hiromi OBAMA

Head, Cooperative Research Team

Hiromi OBAMA

29

StaffTachikawa Administration Department• NIPR Planning and Coordination Division —————————————————————————————————————————————————————————————
Manager

Kumiko TANSHO

Deputy Manager

Motokazu TOYODA

Head, NIPR Planning and Coordination Team• ISM Planning and Coordination Division —————————————————————————————————————————————————————————————
ManagerHead, ISM Planning and Coordination Team

Mitsuo HAMADA

Deputy Manager

Hisayuki IGARASHI

Ayako NARITA

Council of The Institute of Statistical Mathematics ( As of April 1, 2021)
Keiko TAKAHASHI

Senior Researcher (Professor), Global Consolidated Research Institute for Science Wisdom,
Comprehensive Research Organization, Waseda University

Hiroshi MARUYAMAPFN Fellow, Preferred Networks, Inc.

Iguchi SATORU

Professor, Vice-Director General (on Program), National Astronomical Observatory of Japan,
National Institutes of Natural Sciences

Fumiyasu KOMAKI
Masayuki UCHIDA
Hiroshi SAIGO
Yasuhiro OMORI

Professor, Graduate School of Information Science and Technology, The University of Tokyo

Kikuo MAEKAWA

Professor, Spoken Language Division,
Director, Center for Corpus Development National Institute for Japanese Language and Linguistics
Adjunct Researcher, Artificial Intelligence Research Center, Department of Information Technology and Human Factors,
National Institute of Advanced Industrial Science and Technology

Hideki ASOH
Mihoko MINAMI
Yoshinori KAWASAKI
Satoshi YAMASHITA
Yoshihiko MIYASATO
Tomoko MATSUI
Koji KANEFUJI
Satoshi ITO
Genta UENO
Satoshi KURIKI
Kenji FUKUMIZU
Hironori FUJISAWA

Professor, Graduate School of Engineering Science, Osaka University
Professor, Faculty of Political Science and Economics, Waseda University
Professor, Faculty of Economics, University of Tokyo

Professor, Department of Mathematics, Faculty of Science and Technology, Keio University
Professor ( Vice Director-General, ISM )Professor ( Vice Director-General, ISM )Professor ( Vice Director-General, ISM )Professor ( Director of Department of Statistical Modeling, ISM )Professor ( Director of Department of Statistical Data Science, ISM )Professor ( Director of Department of Mathematical Analysis and Statistical Inference, ISM )Professor ( Director of Center for Engineering and Technical Support, ISM )Professor ( Director of School of Statistical Thinking, ISM )Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Cooperative Research Committee ( As of June 1, 2021)
Toshikazu KITANO
Shido SAI
Takashi SEO
Nagatomo NAKAMURA
Hiroki MASUDA
Tomoko MATSUI
Kenichiro SHIMATANI
Masayuki HENMI
Hiroko NAKANISHI

Professor, Department of Architecture, Civil Engineering and Industrial Management Engineering, Nagoya Institute of Technology
Professor, Department of Economics, Faculty of Economics, Okayama Shoka University
Professor, Department of Applied Mathematics, Faculty of Science Division I, Tokyo University of Science
Professor, Faculty of Economics and Business Administration, Sapporo Gakuin University
Professor, Department of Mathematical Sciences, Faculty of Mathematics, Kyushu University
Professor ( Director of Department of Statistical Modeling, ISM )Associate Professor ( Department of Statistical Data Science, ISM )Associate Professor ( Department of Statistical Inference and Mathematics, ISM )Project Professor ( School of Statistical Thinking, ISM )Managing Committee of School of Statistical Thinking ( As of April 1, 2021)
Ken KUROKAWA
Manabu KOBAYASHI
Yoshiyuki TAKEUCHI

30

Vice Director/Professor, National Institute of Genetics
Professor, Center for Data Science, Waseda University
Vice Director/Associate Professor, Graduate School of Economics/Center for Mathematical Modeling and Data Science, Osaka University

Organization

Managing Committee of School of Statistical Thinking
Yoshihiko NISHINO
Jinfang WANG
Satoshi KURIKI
Yukito IBA
Yoshinori KAWASAKI
Hiroko NAKANISHI

Director, Advanced Analytics & AI Innovation Division, SAS Institute Japan, Ltd.
Professor/Dean, School of Data Science, Yokohama City University
Director ( School of Statistical Thinking, ISM )Vice Director ( School of Statistical Thinking, ISM )Professor ( Vice Director-General, ISM )Project Professor ( School of Statistical Thinking, ISM )Research Ethics Review Committee ( As of April 1, 2021)
Specialist on epidemiology and social research

Masayuki KANAI

Professor, School of Human Sciences, Senshu University

Specialist on epidemiology and social research

Keiko SATO

Associate Professor, Department of Health Informatics Graduate School of
Medicine & School of Public Health, Kyoto University

Specialist in the field of ethics and law

Hitomi NAKAYAMA
Yutaka KURIKI
Tadahiko MAEDAYoo Sung PARK
Koji KANEFUJI
Ikuko FUNATOGAWA
Hisashi NOMA

Lawyer, Kasumigaseki-Sogo Law Offices

Person in citizen’s position
Research education staff of ISM
Research education staff of ISM
Research education staff of ISM
Research education staff of ISM
Research education staff of ISM

Kindergarten Director, Nishikokubunji Nursery School
Associate Professor ( Department of Data Science, ISM )Associate Professor ( Department of Data Science, ISM )Professor ( Department of Data Science, ISM )Associate Professor ( Department of Data Science, ISM )Associate Professor ( Department of Data Science, ISM )International Advisory Board ( As of April 1, 2021)
Chun-houh CHEN
Ying CHEN
Arnaud DOUCET
Jaeyong LEE
Donald RICHARDS
Pavel SHEVCHENKO
Alan WELSH

Director and Research Fellow, Institute of Statistical Science, Academia Sinica
Associate Professor, Department of Mathematics, National University of Singapore
Statutory Professor and Professor of Statistics, Department of Statistics, University of Oxford
Professor, Department of Statistics, Seoul National University
Distinguished Professor of Statistics, Department of Statistics, Pennsylvania State University
Professor, Department of Actuarial Studies and Business Analytics, Macquarie Business School,Macquarie University
E.J. Hannan Professor of Statistics, College of Business and Economics, The Australian National University

Advisory Board of NOE Project ( As of June 1, 2021)
Satoshi ITOH
Naonori UEDA
Yasushi OKADA

Chief Coordinator, Foundation for Computational Science

Masafumi KAMACHI

Project Principal Engineer, Information Engineering Program, Research Institute for Value-Added-Information Generation,
Japan Agency for Marine-Earth Science and Technology ( JAMSTEC )Yoshimichi SATO
Ken MATSUSHITA
Yoshiki YAMAGATA

Department Head and Professor, Faculty of Humanities, Kyoto University of Advanced Science

Vice Director, RIKEN Center for Advanced Intelligence Project ( AIP)
President, The Japan Pharmaceutical Manufacturers Association

Director-General, Institute for Monetary and Economic Studies, Bank of Japan
Professor, Graduate School of System Design and Management, Keio University

Professor Emeritus ( As of April 1, 2021)
Sigeki NISIHIRA
Noboru OHSUMI
Masami HASEGAWA
Yasumasa BABA
Yosihiko OGATA
Takashi NAKAMURA
Ryozo YOSHINO

Tatsuzo SUZUKI
Masakatsu MURAKAMI
Yoshiyuki SAKAMOTO
Katsuomi HIRANO
Hiroe TSUBAKI
Yoshiyasu TAMURA
Shinto EGUCHI

Giitiro SUZUKI
Kunio TANABE
Takemi YANAGIMOTO
Masaharu TANEMURAGenshiro KITAGAWA
Tomoyuki HIGUCHI

Ryoichi SHIMIZU
Tadashi MATSUNAWA
Yoshiaki ITOH
Makio ISHIGURO
Nobuhisa KASHIWAGI
Junji NAKANO

31

History

1944

June

Based on a proposal submitted at an academic study conference in December 1943, the organization was
founded as an institute under the direct control of the Ministry of Education. This proposal aimed to provide
supervision for studies looking into the mathematical principles of probability and their application, and was
also intended to facilitate, unify and promote the publication of research results.

1947

April

The affiliated statistical specialists’ school was opened.

May

The Institute was divided into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. (statisticaltheories for the natural sciences), and the 3rd Research Dept. ( statistical theories for the social sciences ).

1949

June

The Institute was placed under the control of the Ministry of Education because of the enforcement of the Ministryof Education Establishment Law.

1955

September

Reorganized into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. ( natural and social
science theories ), and the 3rd Research Dept. ( operations research, statistical analysis theories ). The laboratory
system, comprising 9 laboratories and the research guidance promotion room, was adopted.

1969

October

1971

April

The 4th Research Dept. (informatics theories) was instituted.

1973

April

The 5th Research Dept. (prediction and control theories) was instituted.

1975

October

1979

November

1985

April

Repositioned as a National Inter-University Research Institute due to the regulation change. The new mission
includes providing facilities and skills to other universities, in addition to conducting cutting-edge research on
statistical mathematics. Accordingly, the institute was reorganized into four basic research departments (Fundamental Statistical Theory, Statistical Methodology, Prediction & Control, and Interdisciplinary Statistics ) andtwo strategic centers ( Statistical Data Analysis Center and Statistical Education & Information Center ). The
Statistical Technical Training Center was terminated.

1988

October

The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science, part of the
Graduate University for Advanced Studies ( SOKENDAI ).

1989

June

The Institute was reorganized as an Inter-University Research Institute based on the National School Establishment Law.

1993

April

The Planning Coordination Chief System was instituted.

1997

April

The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of StatisticalComputing, and the Statistical Education and Information Center was reorganized into the Center for Information
on Statistical Sciences.

2003

September

2004

April

The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organi­zation ofInformation and Systems of the Inter-University Research Institute based on the National University Corporation Law. The Planning Coordination Chief System was abolished and the position of Vice Director-General was
instituted instead. The Dept. of Statistical Science in the School of Mathe­matical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical Science and the School of Multidisciplinary Scienceswere instituted.

2005

April

The research organization was reorganized into three research departments ( the Department of Statis­tical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical Inference ). The affiliated Center for Development of Statistical Computing, the Center for Information on Statistical
Sciences, and the Engineering and Technical Services Section were inte­grated into the Center for Engineeringand Technical Support. The Risk Analysis Research Center was instituted.

2006

April

The Administration Planning Coordination Unit was instituted.

2008

April

The Research Innovation Center was instituted.

A new office building was constructed in Minato Ward.

The 6th Research Dept. (statistical theories of human behavior) was instituted.
The Information Research Building was constructed.

The Prediction and Knowledge Discovery Research Center was instituted.

The Administration Planning and Coordination Unit was reorganized into the Administration Planning and Coordination Section ( hereafter APCS), within which the Intellectual Property Unit, the Evaluation Unit and the Information and Public Relations Unit were instituted.
2009

32

January

The Planning Unit was instituted within APCS.

October

The Institute was moved to 10-3 Midori-cho, Tachikawa, Tokyo.

2010

June

Officially opened the Akaike Guest House.

July

Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch the General
Service Center. The NOE Forwarding Unit ( now we call “NOE Promotion Unit” ) was instituted within APCS.

2011

January

Research and Development Center for Data Assimilation was instituted.
Survey Science Center was instituted.

2012

January

Research Center for Statistical Machine Learning, Service Science Research Center and School of StatisticalThinking were instituted.

2014

July
December

2017

The URA Station was instituted within the Planning Unit.
The Office of Female Researcher Development was instituted within the Planning Unit.

January

Survey Science Center and Service Science Research Center were closed.

July

Data Science Center for Creative Design and Manufacturing was instituted.

December

The International Affairs Unit were instituted, and the Gender Equality Unit, which had been within the Planning
Unit, reorganized within APCS.
The Intellectual Property Unit was reorganized as the Industry-Academia Collaboration and Intellectual Property
Unit within APCS.

2018

April

Research Center for Medical and Health Data Science was instituted.
The NIPR/ISM Joint Administration Office was reorganized as the Tachikawa Administration Department of
the Research Organization of Information and Systems ( ROIS ).

2019

March

2021

April

Research and Development Center for Data Assimilation was closed.
ISM Planning and Coordination Division was instituted within Tachikawa Administration Department.

33

Access to the ISM

Tachikawa Bus— Tachikawa Academic Plaza bus stop
— 5 min walk from Saibansho-mae
or Tachikawa-Shiyakusho bus stop
Tama Monorail—10 min walk from Takamatsu Sta.
JR Chuo Line
—25 min walk from Tachikawa Sta.

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Tel: +81- ( 0 ) 50-5533-8500 Fax: +81- ( 0 ) 42-527-9302

https://www.ism.ac.jp/